{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:IRWYG26UPCNHO76OP7AMDOMVTI","short_pith_number":"pith:IRWYG26U","canonical_record":{"source":{"id":"2506.03910","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-04T13:04:29Z","cross_cats_sorted":[],"title_canon_sha256":"83a0847cedff77397819a07070d1eb869113380c7a420886c588346d208db5d2","abstract_canon_sha256":"2adae1dd551063cbab344f843463324eea9b16157405bd206af3b5ba48dd95f5"},"schema_version":"1.0"},"canonical_sha256":"446d836bd4789a777fce7fc0c1b9959a1b47ea9a32da8b6fc258a6ccefec7db0","source":{"kind":"arxiv","id":"2506.03910","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.03910","created_at":"2026-07-05T11:15:53Z"},{"alias_kind":"arxiv_version","alias_value":"2506.03910v1","created_at":"2026-07-05T11:15:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.03910","created_at":"2026-07-05T11:15:53Z"},{"alias_kind":"pith_short_12","alias_value":"IRWYG26UPCNH","created_at":"2026-07-05T11:15:53Z"},{"alias_kind":"pith_short_16","alias_value":"IRWYG26UPCNHO76O","created_at":"2026-07-05T11:15:53Z"},{"alias_kind":"pith_short_8","alias_value":"IRWYG26U","created_at":"2026-07-05T11:15:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:IRWYG26UPCNHO76OP7AMDOMVTI","target":"record","payload":{"canonical_record":{"source":{"id":"2506.03910","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-04T13:04:29Z","cross_cats_sorted":[],"title_canon_sha256":"83a0847cedff77397819a07070d1eb869113380c7a420886c588346d208db5d2","abstract_canon_sha256":"2adae1dd551063cbab344f843463324eea9b16157405bd206af3b5ba48dd95f5"},"schema_version":"1.0"},"canonical_sha256":"446d836bd4789a777fce7fc0c1b9959a1b47ea9a32da8b6fc258a6ccefec7db0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:15:53.915226Z","signature_b64":"3BiqgGpKw4Bw2/lu7sg9vBXLjZv4c39RYk+zv0Ud1uCzVxxqiLF5NP/xlz8F8OPv81yAg9B24kWeSlJ0q6FBBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"446d836bd4789a777fce7fc0c1b9959a1b47ea9a32da8b6fc258a6ccefec7db0","last_reissued_at":"2026-07-05T11:15:53.914660Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:15:53.914660Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.03910","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:15:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9ezVZFg1gcnAvaikGOWsGsZ+rzy94nFRLhlxf6EnTfbyqtQPnYSR+TGxqO6SawnyPvOFfP4CgJWpvjFh340EDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T07:18:23.433466Z"},"content_sha256":"2fdc00218bb6b96de4758a9f3387b5b8aec21e44d7f9e7955458acf9655057f2","schema_version":"1.0","event_id":"sha256:2fdc00218bb6b96de4758a9f3387b5b8aec21e44d7f9e7955458acf9655057f2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:IRWYG26UPCNHO76OP7AMDOMVTI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Enhancing Experimental Efficiency in Materials Design: A Comparative Study of Taguchi and Machine Learning Methods","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Antov Selwinston, P Akshay Kumar, Pavan Taduvai, Rohit Batra, Shreya Bairi, Shyam Prabhu","submitted_at":"2025-06-04T13:04:29Z","abstract_excerpt":"Materials design problems often require optimizing multiple variables, rendering full factorial exploration impractical. Design of experiment (DOE) methods, such as Taguchi technique, are commonly used to efficiently sample the design space but they inherently lack the ability to capture non-linear dependency of process variables. In this work, we demonstrate how machine learning (ML) methods can be used to overcome these limitations. We compare the performance of Taguchi method against an active learning based Gaussian process regression (GPR) model in a wire arc additive manufacturing (WAAM)"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.03910","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2506.03910/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:15:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HlevJ+NJsYFrrCHdIm8tnBA6amblPKCFvpygraIzZYHyngxAfP7wcnvCrotDaOdWz5VxQbMynt6beOMFqqJrCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T07:18:23.434057Z"},"content_sha256":"f1fba826514a105c64a18bed9cb1a7b9d3fe8dac2678730bc5fcf46464ed3895","schema_version":"1.0","event_id":"sha256:f1fba826514a105c64a18bed9cb1a7b9d3fe8dac2678730bc5fcf46464ed3895"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IRWYG26UPCNHO76OP7AMDOMVTI/bundle.json","state_url":"https://pith.science/pith/IRWYG26UPCNHO76OP7AMDOMVTI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IRWYG26UPCNHO76OP7AMDOMVTI/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-08T07:18:23Z","links":{"resolver":"https://pith.science/pith/IRWYG26UPCNHO76OP7AMDOMVTI","bundle":"https://pith.science/pith/IRWYG26UPCNHO76OP7AMDOMVTI/bundle.json","state":"https://pith.science/pith/IRWYG26UPCNHO76OP7AMDOMVTI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IRWYG26UPCNHO76OP7AMDOMVTI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:IRWYG26UPCNHO76OP7AMDOMVTI","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"2adae1dd551063cbab344f843463324eea9b16157405bd206af3b5ba48dd95f5","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-04T13:04:29Z","title_canon_sha256":"83a0847cedff77397819a07070d1eb869113380c7a420886c588346d208db5d2"},"schema_version":"1.0","source":{"id":"2506.03910","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.03910","created_at":"2026-07-05T11:15:53Z"},{"alias_kind":"arxiv_version","alias_value":"2506.03910v1","created_at":"2026-07-05T11:15:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.03910","created_at":"2026-07-05T11:15:53Z"},{"alias_kind":"pith_short_12","alias_value":"IRWYG26UPCNH","created_at":"2026-07-05T11:15:53Z"},{"alias_kind":"pith_short_16","alias_value":"IRWYG26UPCNHO76O","created_at":"2026-07-05T11:15:53Z"},{"alias_kind":"pith_short_8","alias_value":"IRWYG26U","created_at":"2026-07-05T11:15:53Z"}],"graph_snapshots":[{"event_id":"sha256:f1fba826514a105c64a18bed9cb1a7b9d3fe8dac2678730bc5fcf46464ed3895","target":"graph","created_at":"2026-07-05T11:15:53Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2506.03910/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Materials design problems often require optimizing multiple variables, rendering full factorial exploration impractical. Design of experiment (DOE) methods, such as Taguchi technique, are commonly used to efficiently sample the design space but they inherently lack the ability to capture non-linear dependency of process variables. In this work, we demonstrate how machine learning (ML) methods can be used to overcome these limitations. We compare the performance of Taguchi method against an active learning based Gaussian process regression (GPR) model in a wire arc additive manufacturing (WAAM)","authors_text":"Antov Selwinston, P Akshay Kumar, Pavan Taduvai, Rohit Batra, Shreya Bairi, Shyam Prabhu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-04T13:04:29Z","title":"Enhancing Experimental Efficiency in Materials Design: A Comparative Study of Taguchi and Machine Learning Methods"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.03910","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:2fdc00218bb6b96de4758a9f3387b5b8aec21e44d7f9e7955458acf9655057f2","target":"record","created_at":"2026-07-05T11:15:53Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"2adae1dd551063cbab344f843463324eea9b16157405bd206af3b5ba48dd95f5","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-04T13:04:29Z","title_canon_sha256":"83a0847cedff77397819a07070d1eb869113380c7a420886c588346d208db5d2"},"schema_version":"1.0","source":{"id":"2506.03910","kind":"arxiv","version":1}},"canonical_sha256":"446d836bd4789a777fce7fc0c1b9959a1b47ea9a32da8b6fc258a6ccefec7db0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"446d836bd4789a777fce7fc0c1b9959a1b47ea9a32da8b6fc258a6ccefec7db0","first_computed_at":"2026-07-05T11:15:53.914660Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:15:53.914660Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3BiqgGpKw4Bw2/lu7sg9vBXLjZv4c39RYk+zv0Ud1uCzVxxqiLF5NP/xlz8F8OPv81yAg9B24kWeSlJ0q6FBBA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:15:53.915226Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.03910","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2fdc00218bb6b96de4758a9f3387b5b8aec21e44d7f9e7955458acf9655057f2","sha256:f1fba826514a105c64a18bed9cb1a7b9d3fe8dac2678730bc5fcf46464ed3895"],"state_sha256":"65ddbd69119d627cf92297638a4cb0da8c0cee35de8bfbbf26809fcfbbcd29a0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Cp7lt2VLicgWAfGEGe5oVXu/BE3gb94LYf3UT961joApNIgCZu9sgPzMJJYaa4iMfcfv5rp1fFqj0lLjhDZQBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T07:18:23.438116Z","bundle_sha256":"801259483b6a40efd1f82feafbcee7951177d8a749dc630778ba85ba8f738902"}}